Bibliographic Details
| Title: |
Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture |
| Authors: |
Fatma M. Talaat, Shaker El-Sappagh, Khaled Alnowaiser, Esraa Hassan |
| Source: |
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-14 (2024) |
| Publisher Information: |
BMC, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
Prostate cancer detection, Convolution neural network, ResNet50, Dual optimizer, Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
Abstract Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1472-6947 |
| Relation: |
https://doaj.org/toc/1472-6947 |
| DOI: |
10.1186/s12911-024-02419-0 |
| Access URL: |
https://doaj.org/article/2f77caa2b6e24d7fa24084d6624579f6 |
| Accession Number: |
edsdoj.2f77caa2b6e24d7fa24084d6624579f6 |
| Database: |
Directory of Open Access Journals |
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